Paper: New Word Detection for Sentiment Analysis

ACL ID P14-1050
Title New Word Detection for Sentiment Analysis
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Automatic extraction of new words is an indispensable precursor to many NLP tasks such as Chinese word segmentation, named entity extraction, and sentimen- t analysis. This paper aims at extract- ing new sentiment words from large-scale user-generated content. We propose a ful- ly unsupervised, purely data-driven frame- work for this purpose. We design statisti- cal measures respectively to quantify the utility of a lexical pattern and to measure the possibility of a word being a newword. The method is almost free of linguistic re- sources (except POS tags), and requires no elaborated linguistic rules. We also demonstrate how new sentiment word will benefit sentiment analysis. Experiment re- sults demonstrate the effectiveness of the proposed method.